We developed a short-course for graduate students in MIT’s School of Chemical Engineering Practice School to equip them with the critical skills required to successfully complete the program’s four one-month projects. The course covers project management, problem solving, safety, professional behavior, time management, career development, and the basics of capital cost estimating and project valuation.

To enhance student engagement, we experimented with A.I.-generated case studies. Writing them from scratch proved too time-consuming, so we turned to ChatGPT. We uploaded the Practice School handbook and other program details to provide Chat with background, then asked it to generate a two-page case study requiring students to apply course skills. While students couldn’t solve the technical problems directly, they could outline their approach based on what they had learned.

Below is the first case study ChatGPT created. I was amazed—it generated the content instantly. A few prompt adjustments were needed, including adding personal conflicts as seen toward the end.

Testing this with students proved very effective. They returned with an action plan, sparking discussions on strategy and planning—especially crucial for experiment-based work requiring preparation time.

With the power of A.I. now trained to our requirements, we’re excited to generate many more case studies at the click of a button, spanning the technology pipeline, each requiring unique problem-solving approaches.

Have you used A.I. to enhance education? Share your thoughts in the comments!

ChatGPT Case Study

You are the leader of a 3-person Practice School team.  You receive the following problem statement on Monday afternoon of Week 1.  You will be delivering your project proposal on Friday morning of Week 1.  What actions will you take and when will you take them?  Do not limit yourself to the first week only.

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Problem Statement #1: Optimizing Catalyst Performance in Biofuel Production

Company: EcoSynth Biofuels Inc.

Background: EcoSynth Biofuels Inc., a recent start-up, specializes in the production of biofuels using a proprietary catalytic process to convert biomass into high-energy biofuels. The company has developed a catalytic process that has shown promise in lab-scale tests, achieving a conversion efficiency of 85%. However, scaling up the process to a commercial production level has revealed several operational challenges that have hindered consistent performance and efficiency.

Problem: During the commercial plant operations, the catalytic reactor has exhibited fluctuating performance, with conversion efficiencies varying between 70% and 85%. This inconsistency is believed to be due to several factors, including suboptimal reaction conditions, catalyst deactivation, and uneven biomass feedstock quality. These fluctuations are impacting the overall yield and economic viability of the biofuel production process.

Key issues and observations include:

1. Feedstock Variability: Different batches of biomass have shown significant variation in moisture content, ash content, and chemical composition. This variability seems to correlate with fluctuations in conversion efficiency, but no comprehensive analysis has been conducted to confirm this.
2. Reaction Conditions: The reactor has shown sensitivity to small changes in temperature and pressure, causing swings in conversion efficiency. There is a lack of detailed data on the optimal operational parameters for maintaining consistent performance.
3. Catalyst Deactivation: Preliminary analysis suggests the catalyst may be deactivating due to fouling or poisoning, but the exact cause has not been determined. There have been instances where the catalyst performance dropped unexpectedly, requiring frequent replacements.
4. Economic Pressure: The business manager has imposed a strict deadline and budget constraints, requiring a solution that balances technical effectiveness with cost-efficiency.

Resources Available: The students will have access to the following resources to complete their project:

• Commercial Plant Operations: The students will have full access to the commercial unit where the process is currently operating. They will be able to run experiments, collect data, and make adjustments as needed. However, access to the unit is limited to certain hours due to ongoing production schedules, and there are strict safety protocols that must be followed.
• Bench-Scale Process: The students will also have access to the bench-scale process that was used during the initial development of the catalytic process.
• Research Reports and Reaction Modeling: Several research reports and reaction modeling efforts are available for review. These documents contain valuable data and insights but are somewhat disorganized and inconsistent.

Objective: The primary objective is to identify what is required to stabilize and optimize the catalytic reactor performance to consistently achieve or exceed a conversion efficiency of 85%.

Key Personnel:

• Dr. John Taylor, Commercial Unit Process Engineer
• Sarah Mitchell, Head of Commercial Unit Operations
• Dr. Emily Chen, Technical Innovator
• Michael Roberts, Business Manager

Conflicting Perspectives:

• Dr. John Taylor, Commercial Unit Process Engineer: Attributes the performance issues primarily to feedstock variability and is pushing for a thorough analysis of the biomass.
• Sarah Mitchell, Commercial Unit Operations: Believes the main issue lies with the operational conditions and is advocating for a detailed study to optimize these parameters.
• Dr. Emily Chen, Technical Innovator: Suspects that the catalyst is deactivating faster than expected due to impurities in the biomass and insists on investigating the catalyst’s lifespan and regeneration techniques.
• Michael Roberts, Business Manager: Focused on delivering quick, cost-effective solutions and is skeptical about extensive research efforts that do not provide immediate results.

Timeline: 4 weeks

Contact: Dr. Jane Smith, Chief Process Engineer, EcoSynth Biofuels Inc.

END